UrbanGround: From Local Perception to Spatial Agency in a Real-Scale City
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Merged summary
TL;DR - UrbanGround is a closed-loop benchmark for testing whether multimodal LLM agents can turn street-level perception into reliable navigation in a physically constrained 3D replica of Hong Kong. Current agents handle basic visual recognition and short-range spatial reasoning, but struggle to sustain and correct goal-directed behavior over longer routes.
- Built from territory-wide 3D geospatial data, the sandbox supports first-person exploration and interactive-map navigation.
- Evaluates active spatial grounding, navigation to increasingly distant or ambiguous destinations, and robustness to route changes and pedestrian motion.
- Orientation and pedestrian-aware movement remain unreliable despite useful local perception capabilities.
- During extended exploration, errors accumulate because agents fail to compose local skills into sustained behavior or recover effectively.
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UrbanGround: From Local Perception to Spatial Agency in a Real-Scale City
TL;DR - UrbanGround is a closed-loop benchmark for testing whether multimodal LLM agents can turn street-level perception into reliable navigation in a physically constrained 3D replica of Hong Kong. Current agents handle basic visual recognition and short-range spatial reasoning, but struggle to sustain and correct goal-directed behavior over longer routes.
- Built from territory-wide 3D geospatial data, the sandbox supports first-person exploration and interactive-map navigation.
- Evaluates active spatial grounding, navigation to increasingly distant or ambiguous destinations, and robustness to route changes and pedestrian motion.
- Orientation and pedestrian-aware movement remain unreliable despite useful local perception capabilities.
- During extended exploration, errors accumulate because agents fail to compose local skills into sustained behavior or recover effectively.